Improvement device, improvement method, and planning system

Abstract

This improvement device comprises: an input unit that receives designation of the priority of each term for an objective function including a plurality of terms; and an adjustment unit that calculates, for each term, the amount of change between the result of applying a prescribed solution to the objective function and the result of applying a comparative solution, which is a rearranged function, to the objective function, and adjusts, when the amount of change in a first priority term is smaller than the sum of the amounts of change in terms with a lower priority than the first priority, the objective function such that the amount of change in the first priority term is larger than the sum of the amounts of change in the terms with a lower priority than the first priority.

Description

Improvement device, improvement method, and planning system

[0001] This disclosure relates to an improvement device, an improvement method, and a planning system.

[0002] For example, technologies are being considered for creating work plans that improve the efficiency of various tasks, such as picking operations in warehouses, delivery operations, or production operations in manufacturing. Patent Document 1 discloses a configuration in which the business objective function is defined as a weighted linear sum of a weight coefficient wj and evaluation values ​​Vji of a group of evaluation values, and the group of evaluation values ​​that gives the business objective function the maximum value is recognized as the group of evaluation values ​​with the highest evaluation.

[0003] Japanese Patent Publication No. 2022-8955

[0004] However, Patent Document 1 requires skilled technicians to manually adjust the weight coefficients of the objective function through trial and error, making it difficult to use.

[0005] Therefore, this disclosure aims to provide a technology that can automatically determine the weight coefficients of the objective function.

[0006] One aspect of the present disclosure provides an improvement device comprising: an input unit that accepts the specification of the priority of each term for an objective function including a plurality of terms; and an adjustment unit that calculates the amount of change for each term between the result of applying a predetermined solution to the objective function and the result of applying a comparative solution obtained by rearranging the solution to the objective function, and adjusts the objective function such that if the amount of change of the term with first priority is less than the sum of the amounts of change of the terms with lower priority than the first priority, the amount of change of the term with first priority is greater than the sum of the amounts of change of the terms with lower priority than the first priority.

[0007] One aspect of this disclosure provides an improvement method that accepts the designation of the priority of each term in an objective function including multiple terms, calculates the change in each term between the result of applying a predetermined solution to the objective function and the result of applying a comparative solution obtained by rearranging the solution to the objective function, and adjusts the objective function such that if the change in the term with first priority is less than the sum of the changes in the terms with lower priority than the first priority, the change in the term with first priority is greater than the sum of the changes in the terms with lower priority than the first priority.

[0008] One aspect of the present disclosure provides a planning device comprising: an input unit that accepts the specification of the priority of each term for an objective function including a plurality of terms; an adjustment unit that calculates the amount of change for each term between the result of applying a predetermined solution to the objective function and the result of applying a comparison solution obtained by rearranging the solution to the objective function, and adjusts the objective function such that if the amount of change of the term with first priority is less than the sum of the amounts of change of the terms with priority lower than the first priority, the amount of change of the term with first priority is greater than the sum of the amounts of change of the terms with priority lower than the first priority; an improvement determination unit that updates the solution to the comparison solution if the result of applying the comparison solution to the objective function is an improvement over the result of applying the solution to the objective function; and a planning generation unit that generates a plan based on the solution obtained after repeating the processing by the improvement determination unit and the processing by the adjustment unit a plurality of times.

[0009] These comprehensive or specific embodiments may be implemented as systems, devices, methods, integrated circuits, computer programs, or recording media, or as any combination of systems, devices, methods, integrated circuits, computer programs, and recording media.

[0010] According to this disclosure, the weight coefficients of the objective function can be automatically determined.

[0011] A block diagram showing a first example of the planning device according to Embodiment 1. A flowchart showing the processing in the first example of the planning device according to Embodiment 1. A diagram to explain a specific example of the first cycle of the planning loop in calculation example 1 according to Embodiment 1, where Figure 3(a) is a graph showing the amount of change before coefficient adjustment, and Figure 3(b) is a graph showing the amount of change after coefficient adjustment. A diagram to explain a specific example of the second cycle of the planning loop in calculation example 1 according to Embodiment 1, where Figure 4(a) is a graph showing the amount of change before coefficient adjustment, and Figure 4(b) is a graph showing the amount of change after coefficient adjustment. A diagram to explain a specific example of calculation example 2 according to Embodiment 1, where Figure 5(a) is a graph showing the amount of change before coefficient adjustment, and Figure 5(b) is a graph showing the amount of change after coefficient adjustment. A block diagram showing a second example of the planning device according to Embodiment 1. A flowchart showing the processing in the second example of the planning device according to Embodiment 1. An explanation of a specific example of calculation example 3 according to Embodiment 1. Figure 8(a) is a graph showing the amount of change before normalization, Figure 8(b) is a graph showing the amount of change after normalization and before coefficient adjustment, and Figure 8(c) is a graph showing the amount of change after normalization and after coefficient adjustment. Figure 8(a) is a figure for explaining an example of the configuration of solution x according to Embodiment 1. Figure 8(b) is a figure showing the amount of change after normalization and before coefficient adjustment, and Figure 8(c) is a graph showing the amount of change after normalization and after coefficient adjustment. Figure 8(c) is a figure for explaining an example of the configuration of solution x according to Embodiment 1. Figure 8(b) is a figure showing an example of the generation result of a work plan according to Embodiment 1. Figure 8(c) is a graph showing the change of each term when automatic coefficient adjustment is applied and the planning loop is repeated according to Embodiment 1. Figure 8(c) is a graph showing the change of each term when a skilled technician manually adjusts the coefficients and the planning loop is repeated. Figure 8(c) is a figure for explaining an example of the configuration of solution x according to Embodiment 1. Figure 8(c) is a figure showing an example of the generation result of a work plan according to Embodiment 1. Figure 8(c) is a graph showing the change of each term when automatic coefficient adjustment is applied and the planning loop is repeated according to Embodiment 1

[0012] Embodiments of the present disclosure will be described in detail below, with appropriate reference to the drawings. However, descriptions that are unnecessarily detailed may be omitted. For example, detailed descriptions of already well-known matters and redundant descriptions of substantially identical configurations may be omitted. This is to avoid the following description becoming unnecessarily verbose and to facilitate understanding for those skilled in the art. The accompanying drawings and the following description are provided to enable those skilled in the art to fully understand the present disclosure and are not intended to limit the subject matter of the claims. The functions of one configuration shown in this embodiment may be realized by two or more physical configurations, or the functions of two or more configurations may be realized by, for example, one physical configuration.

[0013] (Embodiment 1) <First example of a planning device> Figure 1 is a block diagram showing the first example of a planning device according to Embodiment 1.

[0014] The planning device 10 is a device for formulating work plans, and its functions include an initial planning unit 11, a plan rearrangement unit 12, an improvement determination unit 13, and a plan generation unit 18. These functions may be realized by the processor 1001 (see Figure 18) of the planning device 10 working in cooperation with a memory 1002 (see Figure 18) to execute a program.

[0015] The initial planning unit 11 obtains a list of tasks and selects the initial best solution x from the list of tasks. ｂｅｓｔ Create.

[0016] The reconfiguration unit 12 is the initial best solution x ｂｅｓｔ Or the best solution x improved by the improvement determination unit 13 ｂｅｓｔ For example, by swapping assignments between two randomly selected workers, or by randomly rearranging the order within a single worker, the best solution x can be found. ｂｅｓｔ Comparative solution x based on ｔｍｐ Create.

[0017] The improvement determination unit 13 determines the comparison solution x created in the plan rearrangement unit 12. ｔｍｐ The improvement determination unit 13 determines whether the objective function is being improved. In addition, the improvement determination unit 13 determines the best solution x during the iterative process. ｂｅｓｔis input to the planning reorganization unit 12. Further, the improvement determination unit 13 outputs the final best solution x after the iterative process. ｂｅｓｔ Note that the improvement determination unit 13 may be configured as an improvement device.

[0018] The plan generation unit 18 generates a work plan from the best solution x output from the improvement determination unit 13. Note that the plan generation unit 18 may display this work plan on the display device 1005 (see FIG. 18). ｂｅｓｔ

[0019] The improvement determination unit 13 includes an input unit 14, an objective function unit 15, and a coefficient automatic adjustment unit 16.

[0020] The input unit 14 receives the specification of the priority of each term f(x) in the objective function f(x) including a plurality of terms. The input unit 14 may display an input screen (see FIGS. 13 to 17) for receiving the specification of this priority on the display device 1005. In the present embodiment, the objective function is defined as follows. i is an integer of 1 or more. Also, w is a weight coefficient. f(x)=Σ wf(x). The objective function unit 15 executes the calculation regarding the objective function f(x) using the coefficient w automatically adjusted by the coefficient automatic adjustment unit 16. Also, the objective function unit 15 calculates the change amount |Δwf(x)| of each term. ａｌｌ ｉ ｉ ａｌｌ ｉ ｉ ｉ ｉ ａｌｌ ｉ ｉ

[0021] The coefficient automatic adjustment unit 16 automatically adjusts the weight coefficient w. The method of automatically adjusting the weight coefficient will be described later. Also, when there is a bias in the ease of change of values when the solution x changes among the terms of the objective function f(x), the coefficient automatic adjustment unit 16 also normalizes the change amounts of each term. When there is a bias in the ease of change of values, adjustment focusing on the weight coefficient w of the term with an easy change in value is likely to be performed, and the weight coefficients w of other terms ｉ ａｌｌ ｉ ｉAdjustments focusing on this become more difficult. However, since the solution x is applied to multiple terms that make up the objective function, maintaining a balance of the changes in multiple terms makes it easier to obtain the optimal solution x. Therefore, the automatic coefficient adjustment unit 16 maintains this balance by performing normalization, while adjusting the weight coefficient w ｉ Adjust the values. An example of a case where a term is more prone to value changes than other terms is when a term with different units is included. Also, even if the units of the terms are the same, if it is recognized that some terms are more prone to value changes, the user may be allowed to specify which terms to normalize.

[0022] Figure 2 is a flowchart showing the processing in a first example of the planning device 10 according to Embodiment 1.

[0023] The coefficient automatic adjustment unit 16 adjusts the coefficient w ｉ Initialize (S101). Note that the coefficient w ｉ The value of is ultimately automatically adjusted, so coefficient w ｉ The initial value can be any value.

[0024] The input unit 14 processes each term f ｉ The system accepts input for priority and stores the input priority in memory 1002 (see Figure 18) (S102). For example, the input unit 14 displays an input screen as shown in Figures 13 to 17 on the display device 1005 and accepts priority input from the user.

[0025] The initial planning unit 11 is the initial best solution x ｂｅｓｔ Create (S103).

[0026] The planning device 10 repeats the following planning loop (S104 to S113) a predetermined number of times (S104).

[0027] The plan rearrangement unit 12 finds the best solution x ｂｅｓｔ Rearrange the elements to get the comparison solution x ｔｍｐ Create (S105).

[0028] The planning device 10 repeats the coefficient loop (S106 to S109) (number of priorities - 1) times, in order from the highest priority term to the lowest priority term (S106).

[0029] The objective function part 15 is the best solution x ｂｅｓｔ Change amount |Δw based on ｉ f ｉ Calculate (x)| (S107). This change is the comparison solution x ｔｍｐ The value of each term in the best solution x ｂｅｓｔ This value indicates how much the value of each corresponding term has changed, and is expressed as an absolute value. That is, the best solution x ｂｅｓｔ Compared solution x ｔｍｐ Between these two points, we do not consider whether the value of each term has increased or decreased, but only the magnitude of the change (amount of change). By using this amount of change, we can find the best solution x ｂｅｓｔ Compared solution x ｔｍｐ In the change between these two points, we can evaluate which terms had the greatest impact.

[0030] The coefficient automatic adjustment unit 16 adjusts the change amount |Δw| calculated in step S107. ｉ f ｉ (x)| is determined to satisfy the satisfaction criterion (S108). The details of the satisfaction criterion will be described later.

[0031] The coefficient automatic adjustment unit 16 adjusts the change amount |Δw| calculated in step S107. ｉ f ｉ If (x) | does not satisfy the satisfaction criterion (S108: NO), the coefficient update formula is applied to update the coefficient (S109), and the process proceeds to step S110. Details of the coefficient update formula will be described later.

[0032] The coefficient automatic adjustment unit 16 adjusts the change amount |Δw| calculated in step S107. ｉ f ｉ If (x)| satisfies the satisfaction criterion (S108: YES), the process proceeds to step S110.

[0033] The planning device 10 repeats the coefficient loop (S106 to S109) a number of times equal to (number of priorities - 1) (S110), and then proceeds to the next step S111.

[0034] The improvement determination unit 13 determines the best solution x ｂｅｓｔ Update condition "f ａｌｌ (x ｂｅｓｔ )>f ａｌｌ(x ｔｍｐ Determine whether the condition is met (S111).

[0035] Best solution x ｂｅｓｔ If the update conditions are met (S111: YES), the improvement determination unit 13 determines x ｂｅｓｔ to x ｔｍｐ The data is updated (S112), and the process proceeds to step S113.

[0036] Best solution x ｂｅｓｔ If the update conditions are not met (S111: NO), the improvement determination unit 13 proceeds to step S113.

[0037] The planning device 10 repeats the planning loop (S104 to S113) a set number of times (S113), and then proceeds to the next step S114.

[0038] The improvement determination unit 13 determines the best solution x ｂｅｓｔ The output is then generated, and the plan generation unit 18 outputs the best solution x ｂｅｓｔ A work plan is generated from this (S114). Then, this process ends.

[0039] <Calculation Example 1> As Calculation Example 1, we will explain a specific example of the process shown in Figure 2. Note that Calculation Example 1 explains a specific example where there are no terms with equal priority and no normalization is performed on the change amount of each term.

[0040] Figure 3 is a diagram illustrating a specific example of the first iteration of the planning loop in Calculation Example 1 according to Embodiment 1. Figure 3(a) is a graph showing the amount of change before coefficient adjustment, and Figure 3(b) is a graph showing the amount of change after coefficient adjustment.

[0041] First, let's explain each variable in Calculation Example 1.

[0042] x is the solution to the work plan, and includes, for example, who will be assigned which task, at what time, and the order of the tasks. ｂｅｓｔ This shows the best solution, x ｔｍｐ This shows the comparative solution.

[0043] lol １ , lol ２ , lol ３ w is the weighting coefficient. １, w ２ , w ３ may simply be called a coefficient. In step S101, the initial weight coefficients are set as w １ = 1.0, w ２ = 1.0, w ３ = 1.0.

[0044] f １ (x), f ２ (x), f ３ (x) is an evaluation value or a penalty value. Note that f １ (x), f ２ (x), f ３ (x) may be called a term.

[0045] The objective function is f ａｌｌ (x) = w １ f １ (x) + w ２ f ２ (x) + w ３ f ３ (x). Based on this objective function f ａｌｌ (x), the goodness or badness of the modified plan (comparison solution x ｔｍｐ ) is evaluated. In this embodiment, the smaller the value of the objective function f ａｌｌ (x), the better the plan (more efficient plan), and the larger the value of the objective function f ａｌｌ (x), the worse the plan (less efficient plan). However, the content of this disclosure is also applicable when the larger the value of the objective function f ａｌｌ (x), the better the plan (more efficient plan), and the smaller the value of the objective function f ａｌｌ (x), the worse the plan (less efficient plan).

[0046] In calculation example 1, the priorities are set as f １ (x) term > f ２ (x) term > f ３ (x) term. In this embodiment, this priority is expressed as [1, 2, 3]. That is, in calculation example 1, f １ (x) is minimized with the highest priority.

[0047] c is a constant, and in this embodiment, c = 0.9.

[0048] <Calculation Example 1: Satisfaction Judgment Before Coefficient Update (First Round)> In the first round of the planning loop, the objective function part 15 uses the coefficients before update to calculate the change amount |Δw ｂｅｓｔ f ｉ f ｉ (x)| for each term at the current best solution x.

[0049] As shown in the graph of Fig. 3(a), assume that the changes in each term at the current time are as follows. Δw １ f １ (x) = +1 Δw ２ f ２ (x) = -4 Δw ３ f ３ (x) = -6 Therefore, the change amounts (absolute values) of each term are as follows. |Δw １ f １ (x)| = 1 |Δw ２ f ２ (x)| = 4 |Δw ３ f ３ (x)| = 6 The satisfaction judgment formula is defined as "the change amount of the term with higher priority > the sum of the change amounts of the terms with lower priority". When this satisfaction judgment formula is satisfied, in the objective function f ａｌｌ (x), the influence exerted by the term with higher priority is greater than the total influence exerted by the terms with lower priority. Note that the satisfaction judgment formula can also be defined as "the change amount of the term with higher priority is greater than any of the change amounts of the terms with lower priority". However, in this case, even if each change amount of the terms with lower priority is smaller than the change amount of the term with higher priority, the sum of the change amounts of the terms with lower priority may exceed the change amount of the term with higher priority. In such a situation, in the objective function f ａｌｌ (x), the influence exerted by the term with higher priority becomes smaller than the total influence exerted by all the other terms with lower priority, so it cannot be said that the term with higher priority has a greater influence than the terms with lower priority (the set). Therefore, in this embodiment, the satisfaction judgment formula "the change amount of the term with higher priority > the sum of the change amounts of the terms with lower priority" is used.

[0050] In Calculation Example 1, the satisfaction judgment formula is |Σ ｉ Δw ｉ f ｉ (x)| > Σ ｊ |Δw ｊ f ｊ(x) | (i, j) = ([1], [2, 3]), ([2], [3]) is defined.

[0051] In other words, if the inequality holds when i = [1] and j = [2, 3], and when i = [2] and j = [3], the satisfaction criterion is determined to be satisfied; otherwise, the satisfaction criterion is determined to be not satisfied.

[0052] Here, if i = [1] and j = [2, 3], then the left side is f １ The change in the (x) term is shown, and the right-hand side is f ２ Change in the (x) term and f ３ The sum of the changes in the (x) terms is shown. In other words, the satisfaction criterion in this case is defined as "the change in the first priority term > the sum of the changes in the terms with a lower priority than the first priority." Note that if there is only one term with a lower priority than the first priority, the satisfaction criterion may be defined as "the change in the first priority term > the second priority term which is lower than the first priority."

[0053] Note that the right-hand side of the satisfaction criterion is defined as "the sum of the changes in the low-priority terms," ​​assuming there are multiple low-priority terms. However, if there is only one low-priority term, the change in that low-priority term is treated as "the sum of the changes in the low-priority terms."

[0054] Also, if i = [2] and j = [3], the left side is f ２ The change in the (x) term is shown, and the right-hand side is f ３ The change in the (x) term is shown. In other words, the satisfaction criterion in this case means that "the change in the second-highest priority term > the change in the third-highest priority term or lower."

[0055] Therefore, if the satisfaction criterion is satisfied, f １ (x) term, f ２ (x) term, f ３ For term (x), the relationship "change in high-priority terms > sum of changes in low-priority terms" holds true.

[0056] According to the graph in Figure 3(a), the change in the high-priority term is |Δw|. １ f １(x)| is 1, and the sum of the changes of the low priority |Δw| is 1. ２ f ２ (x) | + | Δw ３ f ３ (x)| is 10, and |Δw １ f １ (x) | < | Δw ２ f ２ (x) | + | Δw ３ f ３ (x)| (i.e., 1 < 10), so the criterion for satisfaction is not met.

[0057] Therefore, the automatic coefficient adjustment unit 16 determines that step S108 is NO, applies the coefficient update formula in step S109, and performs the coefficient update (coefficient adjustment) described below.

[0058] Furthermore, examining the change in the objective function at this time, we find that Δf ａｌｌ (x) = Δw １ f １ (x) + Δw ２ f ２ (x) + Δw ３ f ３ (x) = 1 - 4 - 6 < 0, and f has high priority. １ Despite the (x) term worsening (i.e., increasing), the objective function f ａｌｌ (x) is improving (i.e., decreasing). This means that the higher-priority terms are not having an appropriate impact on the objective function.

[0059] <Calculation Example 1: Coefficient Update (First Cycle)> In step S109, the automatic coefficient adjustment unit 16 automatically adjusts the coefficients so that "the amount of change in high priority > the sum of the amounts of change in low priority".

[0060] In the first iteration of the coefficient loop, the change in the high-priority term is |Δw|. １ f １ (x)| and the sum of the changes in the low-priority terms is (|Δw ２ f ２ (x) | + | Δw ３ f ３ (x)|)

[0061] And, as mentioned above, |Δw １ f １ (x) | ≤ ( | Δw ２f ２ (x)| + |Δw ３ f ３ (x)|) because (that is, because 1 < (4 + 6)), the satisfaction judgment formula is not satisfied.

[0062] Furthermore, |Δw １ f １ (x)| ≠ 0, and |Δw ２ f ２ (x)| + |Δw ３ f ３ (x)| ≠ 0, so the coefficient automatic adjustment unit 16 updates the coefficient w in the following calculation ２ , w ３ to update. w ２ ← w ２ × (|Δw １ f １ (x)| / (|Δw ２ f ２ (x)| + |Δw ３ f ３ (x)|)) × c = 1 × (1 / (4 + 6)) × 0.9 = 0.09 w ３ ← w ３ × (|Δw １ f １ (x)| / (|Δw ２ f ２ (x)| + |Δw ３ f ３ (x)|)) × c = 1 × (1 / (4 + 6)) × 0.9 = 0.09 In the second round of the coefficient loop, the change amount of the term with higher priority is |Δw ２ f ２ (x)|, and the sum of the change amounts of the terms with lower priority is |Δw ３ f ３ (x)|.

[0063] And in the second round of the coefficient loop, when applying w ２ = 0.09, w ３ = 0.09, |Δw ２ f ２ (x)| ≤ |Δw ３ f ３ (x)| because (that is, because (0.09 × 4) < (0.09 × 6)), the satisfaction judgment formula is not satisfied.

[0064] Furthermore, |Δw ２ f２ (x) | ≠ 0, and | Δw ３ f ３ Since (x) ≠ 0, the coefficient automatic adjustment unit 16 calculates the coefficient w in the following way. ３ Update. lol ３ ←w ３ × (|Δw ２ f ２ (x) | / | Δw ３ f ３ (x) |) × c = 0.09 × (0.36 / 0.54) × 0.9 = 0.054 Therefore, the coefficient is w １ = 1.0, w ２ = 0.09, w ３ It will be updated to =0.054.

[0065] <Calculation Example 1: Satisfaction Check After Coefficient Update (First Loop)> The objective function part 15 uses the adjusted coefficients to determine the best solution x at this point. ｂｅｓｔ Change in each term |Δw ｉ f ｉ Calculate (x)|.

[0066] As shown in the graph in Figure 3(b), the adjusted coefficient w １ = 1.0, w ２ = 0.09, w ３ The changes in each term when using =0.054 are as follows: Δw １ f １ (x)=+1.0 Δw ２ f ２ (x)=-0.36 Δw ３ f ３ (x) = -0.324 Therefore, the change in each term (absolute value) is as follows: |Δw １ f １ (x) |=1.0 |Δw ２ f ２ (x) |=0.36 |Δw ３ f ３ (x) | = 0.324 This is the satisfaction criterion |Δw １ f １ (x) | > ( | Δw ２ f ２ (x) | + | Δw ３ f ３ (x)|) satisfies the condition. That is, 1.0 > (0.36 + 0.324).

[0067] When examining the change in the objective function at this time, Δf ａｌｌ (x) = Δw １ f １ (x) + Δw ２ f ２ (x) + Δw ３ f ３ (x) = 1.0 - 0.36 - 0.324 = 0.316 > 0. As a result of the deterioration (i.e., increase) of the f １ (x) term with high priority, the objective function f ａｌｌ has also deteriorated (i.e., increased). That is, the term with high priority has an appropriate influence on the objective function. Therefore, it can be determined that the weight coefficient of the objective function can be adjusted so that the improvement determination of the solution according to the priority can be made.

[0068] Note that since the purpose is to reflect the priority in the weight coefficient, the weight coefficient is updated regardless of the improvement / deterioration of the objective function. Also, in the above case, since the objective function has deteriorated, the best solution is not updated. That is, step S111 becomes NO, and the search for the best solution continues with the objective function after the coefficient update.

[0069] FIG. 4 is a diagram for explaining a specific example of the second round of the planning loop in Calculation Example 1 according to Embodiment 1. FIG. 4(a) is a graph showing the change amount before coefficient adjustment, and FIG. 4(b) is a graph showing the change amount after coefficient adjustment.

[0070] <Calculation Example 1: Satisfaction Judgment before Coefficient Update (Second Round)> In the second round of the planning loop, the current coefficients are w １ = 1.0, w ２ = 0.09, w ３ = 0.054 as calculated above.

[0071] As shown in the graph of FIG. 4(a), the changes in each term at the current time are assumed to be as follows. Δw １ f １ (x) = -1 Δw ２ f ２ (x) = -0.36 Δw ３ f ３ (x) = +1.5. Therefore, the change amounts (absolute values) of each term are as follows. |Δw １ f １(x) | = 1 | Δw ２ f ２ (x) |=0.36 |Δw ３ f ３ (x) | = 1.5 Furthermore, from the above equation, f ２ (x)=-0.36 / Δw ２ =-0.36 / 0.02=4f ３ (x)=+1.5 / Δw ３ = +1.5 / 0.054 ≈ 27.8. If the relationship "change in high-priority terms > sum of changes in low-priority terms" is satisfied, then the evaluation of the solution improvement will take priority into account. However, with the current coefficients, |Δw １ f １ (x) | > ( | Δw ２ f ２ (x) | + | Δw ３ f ３ Since (x) | (i.e., 1 > (0.36 + 1.5)), the satisfaction criterion is not satisfied.

[0072] When we examine the change in the objective function at this time, we find that Δf ａｌｌ (x) = Δw １ f １ (x) + Δw ２ f ２ (x) + Δw ３ f ３ (x) = -1 - 0.36 + 1.5 > 0, and f has high priority. １ Despite the improvement (i.e., reduction) of the (x) term, the objective function f ａｌｌ (x) is worsening (i.e., increasing). This means that the high-priority terms are not having an appropriate impact on the objective function.

[0073] <Calculation Example 1: Coefficient Update (2nd Round)> In step S109, the automatic coefficient adjustment unit 16 automatically adjusts the coefficients so that "the amount of change in the high-priority term > the sum of the amounts of change in the low-priority term".

[0074] At this point, the coefficient w １ = 1.0.

[0075] In the first iteration of the coefficient loop, |Δw １ f １ (x) | ≤ ( | Δw ２ f２ (x) | + | Δw ３ f ３ Since (x)|) (i.e., 1 < (0.36 + 1.5)), the satisfaction criterion is not satisfied. Furthermore, |Δw １ f １ (x) | ≠ 0, and | Δw ２ f ２ (x) | + | Δw ３ f ３ Since (x) ≠ 0, the coefficient automatic adjustment unit 16 calculates the coefficient w in the following way. ２ , lol ３ Update. lol ２ ←w ２ × (|Δw １ f １ (x) | / ( | Δw ２ f ２ (x) | + | Δw ３ f ３ (x) |))×c=0.09×(1 / (0.36+1.5))×0.9=0.044 w ３ ←w ３ × (|Δw1f) １ (x) | / ( | Δw ２ f ２ (x) | + | Δw ３ f ３ (x) |)) × c = 0.054 × (1 / (0.36 + 1.5)) × 0.9 = 0.026 In the second iteration of the coefficient loop, the change in the high-priority term is |Δw ２ f ２ (x)| and the sum of the changes in the low-priority terms is |Δw| ３ f ３ (x)|

[0076] Then, in the second iteration of the coefficient loop, the calculation above was used. ２ = 0.044, w ３ Applying 0.026, |Δw ２ f ２ (x) | ≤ | Δw ３ f ３ Since (x) | (i.e., (0.044 × 4) < (0.026 × 27.8)), the satisfaction criterion is not satisfied.

[0077] Furthermore, |Δw ２ f ２(x) | ≠ 0, and | Δw ３ f ３ Since (x) ≠ 0, the coefficient automatic adjustment unit 16 calculates the coefficient w in the following way. ３ Update. lol ３ ←w ３ × (|Δw ２ f ２ (x) | / | Δw ３ f ３ (x) |) × c = 0.026 × (0.176 / 0.72) × 0.9 = 0.0057 Therefore, the coefficient is w １ = 1.0, w ２ = 0.044, w ３ It will be updated to =0.0057.

[0078] <Calculation Example 1: Satisfaction Criteria after Coefficient Update (2nd Round)> The objective function part 15 is the best solution x at this point, with the adjusted coefficients. ｂｅｓｔ Change in each term |Δw ｉ f ｉ Calculate (x)|.

[0079] As shown in Figure 4(b), the adjusted coefficient w １ = 1.0, w ２ = 0.044, w ３ The changes in each term when using =0.0057 are as follows: Δw １ f １ (x) = -1 Δw ２ f ２ (x)=-0.176 Δw ３ f ３ (x) = +0.158 Therefore, the change in each term (absolute value) is as follows: |Δw １ f １ (x) | = 1 | Δw ２ f ２ (x) |=0.176 |Δw ３ f ３ (x) | = 0.158 This is the satisfaction criterion |Δw １ f １ (x) | > | Δw ２ f ２ (x) | + | Δw ３ f ３ (x) | satisfies the condition. That is, 1.0 > (0.176 + 0.158).

[0080] When we examine the objective function at this point, we find that Δf ａｌｌ (x) = Δw １ f １ (x) + Δw ２ f ２ (x) + Δw ３ f ３ (x) = -1 - 0.176 + 0.158 = -1.018 < 0, and f has a higher priority. １ (x), f ２ Following the improvement (i.e., reduction) of the (x) term, the objective function Δf ａｌｌ (x) has also improved (i.e., decreased). This means that the higher-priority terms are having an appropriate effect on the objective function.

[0081] Therefore, the weight coefficients of the objective function can be adjusted so that the improvement of the solution can be judged according to priority. In this way, upon receiving the improvement of the solution, the improvement judgment unit 13 determines YES in step S111 and, in step S112, determines the best solution x ｂｅｓｔ to x ｔｍｐ Update it.

[0082] <Calculation Example 2> As Calculation Example 2, we will explain a specific example of the process shown in Figure 2. Note that in Calculation Example 2, each term has an equal priority and the case where no normalization is performed on the change amount of each term is explained.

[0083] Figure 5 is a diagram illustrating a specific example in Calculation Example 2 according to Embodiment 1, where Figure 5(a) is a graph showing the amount of change before coefficient adjustment, and Figure 5(b) is a graph showing the amount of change after coefficient adjustment.

[0084] First, let's explain each variable in Calculation Example 2. However, we will omit the explanation for variables that are the same as those in Calculation Example 1.

[0085] In calculation example 2, the initial values ​​of the weight coefficients are set to w1 = 1.0, w2 = 1.0, and w3 = 1.0.

[0086] In calculation example 2, the priority is f １ (x) term = f ２ (x) term > f ３Let this be term (x). In this embodiment, this priority is expressed as [[1,2],3]. That is, the priority of term f1(x) and term f2(x) are equal. In this case, w １ f １ (x) + w ２ f ２ Treat (x) as a single term and minimize it with the highest priority.

[0087] The other variables are assumed to be the same as in Calculation Example 1.

[0088] <Calculation Example 2: Satisfaction Determination Before Coefficient Update> The objective function part uses the coefficients before the update, and the current best solution x ｂｅｓｔ Change in each term |Δw ｉ f ｉ Calculate (x)|.

[0089] As shown in the graph in Figure 5(a), the changes in each term at this point in time are as follows: Δw １ f １ (x) = -1 Δw ２ f ２ (x) = +4 Δw ３ f ３ (x) = -6 Therefore, the change in each term (absolute value) is as follows: |Δw １ f １ (x) | = 1 | Δw ２ f ２ (x) | = 4 | Δw ３ f ３ (x) | = 6 The criterion for satisfaction is the same as in calculation formula 1: "change in high-priority terms > sum of changes in low-priority terms". Therefore, the criterion for satisfaction is |Σ ｉ Δw ｉ f ｉ (x) | >Σ ｊ |Δw ｊ f ｊ (x) | (i, j) = ([1, 2], [3]) is defined. Here, the change in the high-priority term is the sum of equal-priority terms (|Δw). １ f １ (x) + Δw ２ f ２ (x)|)

[0090] In other words, when there are multiple terms with the same priority level, the satisfaction criterion can be applied by treating those terms with the same priority level as a single term. Note that in calculation example 2, there are only two levels of priority, so a comparison of the amount of change between terms with priority levels 2 and below, such as (i, j) = ([2], [3]) in calculation example 1, is not performed.

[0091] Using the values ​​shown in the graph in Figure 4(a), the change in the high-priority term (|Δw) １ f １ (x) + Δw ２ f ２ (x)|) is 3 (=|-1+4|), and the sum of the changes in the lower priority terms |Δw| ３ f ３ (x)| is 6, and (|Δw １ f １ (x) + Δw ２ f ２ (x) |) < | Δw ３ f ３ (x)| (i.e., 3 < 6), so the criterion for satisfaction is not met.

[0092] Therefore, the automatic coefficient adjustment unit 16 determines that step S108 is NO, applies the coefficient update formula in step S109, and performs the coefficient update (coefficient adjustment) described below.

[0093] Furthermore, examining the change in the objective function at this time, we find that Δf ａｌｌ (x) = Δw １ f １ (x) + Δw ２ f ２ (x) + Δw ３ f ３ (x) = (-1 + 4) - 6 < 0, and the higher priority is (w １ f １ (x) + w ２ f ２ Despite (x) worsening (increasing), the objective function f ａｌｌ This indicates an improvement (i.e., a decrease). In other words, the high-priority terms are not having an appropriate impact on the objective function.

[0094] <Calculation Example 2: Coefficient Update> In step S109, the coefficient automatic adjustment unit 16 automatically adjusts the coefficients so that "the change amount of the term with high priority > the sum of the change amounts of the terms with low priority".

[0095] The change amount of the term with high priority is |Δw １ f １ (x) + Δw ２ f ２ (x)|, and the sum of the change amounts of the terms with low priority is |Δw ３ f ３ (x)|.

[0096] And, as described above, since |Δw １ f １ (x) + Δw ２ f ２ (x)| < |Δw ３ f ３ (x)| (that is, since (-1 + 4) < 6), the satisfaction judgment formula is not satisfied.

[0097] Further, since |Δw １ f １ (x) + Δw ２ f ２ (x)| ≠ 0, and |Δw ３ f ３ (x)| ≠ 0, the coefficient automatic adjustment unit 16 updates the coefficient w ３ in the next calculation. w ３ ←w ３ ×((|Δw １ f １ (x) + Δw ２ f ２ (x)|) / |Δw ３ f ３ (x)|)×c = 1×(3 / 6)×0.9 = 0.45. Therefore, the coefficients are updated to w １ = 1.0, w ２ = 1.0, w ３ = 0.45.

[0098] <Calculation Example 2: Satisfaction Judgment after Coefficient Update> The objective function unit 15 calculates the change amount |Δw ｂｅｓｔ of each term at the current best solution x ｉ f ｉ (x)| with the adjusted coefficients.

[0099] As shown in the graph in Figure 5(b), the adjusted coefficient w １ = 1.0, w ２ = 1.0, w ３ The changes in each term when using =0.45 are as follows: Δw １ f １ (x) = -1 Δw ２ f ２ (x) = +4 Δw ３ f ３ (x) = -2.7 Therefore, the change in each term (absolute value) is as follows: |Δw １ f １ (x) | = 1 | Δw ２ f ２ (x) | = 4 | Δw ３ f ３ (x) | = 2.7 This is the satisfaction criterion (|Δw １ f １ (x) + Δw ２ f ２ (x) | > | Δw ３ f ３ (x)|) satisfies the condition. That is, (-1+4) > 2.7.

[0100] When we examine the change in the objective function at this time, we find that Δf ａｌｌ (x) = Δw １ f １ (x) + Δw ２ f ２ (x) + Δw ３ f ３ (x) = -1 + 4 - 2.7 = 0.3 > 0, and the higher priority is (Δw １ f １ (x) + Δw ２ f ２ Due to the deterioration (i.e., increase) of the (x) term, the objective function Δf ａｌｌ (x) has also worsened (i.e., increased). This means that the high-priority terms are having an appropriate influence on the objective function. Therefore, we can conclude that we were able to adjust the weight coefficients of the objective function so that we can judge the improvement of the solution according to its priority.

[0101] <Second Example of the Planning Device> The second example of the planning device 10 describes the case where normalization is performed on terms with different units.

[0102] Figure 6 is a block diagram showing a second example of the planning device 10 according to Embodiment 1.

[0103] The second example of the planning device 10 includes a normalization unit 17 in addition to the functions of the first example of the planning device 10 shown in Figure 1, in addition to the coefficient automatic adjustment unit 16.

[0104] The normalization unit 17 normalizes at least one of the multiple terms included in the objective function by adjusting its coefficient. For example, if the multiple terms included in the objective function include terms with different units, the normalization unit 17 normalizes those terms.

[0105] Figure 7 is a flowchart showing the processing in a second example of the planning device 10 according to Embodiment 1.

[0106] The coefficient automatic adjustment unit 16 adjusts the coefficient w ｉ Initialize (S201).

[0107] The input unit 14 processes each term f ｉ The system accepts input for the priority and stores the input priority in memory 1002 (see Figure 18) (S202). For example, the input unit 14 displays an input screen as shown in Figures 13 to 17 on the display device 1005 and accepts priority input from the user.

[0108] The input unit 14 receives the input of a normalization flag and stores the input normalization flag in the memory 1002 (S203). The normalization flag is a flag that indicates which term should be subjected to normalization. For example, the input unit 14 displays an input screen on the display device 1005 as shown in Figures 14, 16, and 17 and receives the input of a normalization flag from the user.

[0109] The initial planning unit 11 is the initial best solution x ｂｅｓｔ Create (S204).

[0110] The planning device 10 repeats the following planning loop (S205 to S215) a predetermined number of times (S205).

[0111] The plan rearrangement unit 12 finds the best solution x ｂｅｓｔ Rearrange the elements to get the comparison solution x ｔｍｐ Create (S206).

[0112] The planning device 10 repeats the coefficient loop (S207 to S212) (S207) for (number of priorities - 1) times, in order from the highest priority term to the lowest priority term.

[0113] The objective function part 15 is the best solution x ｂｅｓｔ Change amount |Δw based on ｉ f ｉ Calculate (x)| (S208).

[0114] If the amount of change calculated in step S208 is the largest ever, the normalization unit 17 normalizes and updates the coefficient using that largest amount of change (S209). If the amount of change calculated in step S208 is not the largest ever, the normalization unit 17 does not need to normalize the coefficient.

[0115] The objective function section 15 uses the coefficients normalized in step S209 to find the best solution x ｂｅｓｔ Change amount |Δw based on ｉ f ｉ The value of (x)| is calculated (S210). If the coefficients were not normalized in step S209, the objective function section 15 may omit step S210 and use the amount of change calculated in step S208.

[0116] The coefficient automatic adjustment unit 16 adjusts the change amount |Δw| calculated in step S210. ｉ f ｉ (x)| is determined to satisfy the satisfaction criterion (S211).

[0117] The coefficient automatic adjustment unit 16 adjusts the change amount |Δw| calculated in step S210. ｉ f ｉ If (x) | does not satisfy the satisfaction criterion (S211: NO), the coefficient update formula is applied to update the coefficient (S212), and the process proceeds to step S212.

[0118] The coefficient automatic adjustment unit 16 adjusts the change amount |Δw| calculated in step S210. ｉ f ｉ If (x)| satisfies the satisfaction criterion (S211: YES), proceed to step S212.

[0119] The planning device 10 repeats the coefficient loop (S207 to S212) a number of times equal to (number of priorities - 1) (S212), and then proceeds to step S213.

[0120] The improvement determination unit 13 determines the best solution x ｂｅｓｔ Update condition "f ａｌｌ (x ｂｅｓｔ )>f ａｌｌ (x ｔｍｐ Determine whether the condition is met (S213).

[0121] Best solution x ｂｅｓｔ If the update conditions are met (S213: YES), the improvement determination unit 13 determines x ｂｅｓｔ to x ｔｍｐ The data is updated (S214), and the process proceeds to step S215.

[0122] Best solution x ｂｅｓｔ If the update conditions are not met (S213: NO), the improvement determination unit 13 proceeds to step S215.

[0123] The planning device 10 repeats the planning loop (S205 to S215) a set number of times (S215), and then proceeds to the next step S216.

[0124] The improvement determination unit 13 determines the best solution x ｂｅｓｔ The output is then generated, and the plan generation unit 18 outputs the best solution x ｂｅｓｔ A work plan is generated from this (S216). Then, this process is completed.

[0125] <Calculation Example 3> As Calculation Example 3, we will explain a specific example of the process shown in Figure 7. Note that in Calculation Example 3, each term has an equal priority, and we will explain the case where normalization is performed on different units of each term.

[0126] Figure 8 is a diagram illustrating a specific example in calculation example 3 according to embodiment 1. Figure 8(a) is a graph showing the change before normalization, Figure 8(b) is a graph showing the change after normalization and before coefficient adjustment, and Figure 8(c) is a graph showing the change after normalization and after coefficient adjustment.

[0127] First, let's explain each variable in Calculation Example 3. However, we will omit the explanation of variables that are the same as those in Calculation Example 1.

[0128] In calculation example 3, the initial value of the weight coefficient is w １ = 0.5, w ２ = 1.0, w ３ Let's assume it equals 1.0.

[0129] In calculation example 3, the priority is f １ (x) term = f ２ (x) term > f ３ Let this be term (x). In this embodiment, this priority is expressed as [[1,2],3]. That is, f １ (x) term and f ２ Assume that the precedence of the (x) terms is equal. In this case, w １ f １ (x) + w ２ f ２ Treat (x) as a single term and minimize it first.

[0130] The other variables are assumed to be the same as in Calculation Example 1.

[0131] <Calculation Example 3: Normalization> In step S208, the objective function part 15 uses the coefficients before the update to obtain the best solution x at this point in time. ｂｅｓｔ Change in each term |Δw ｉ f ｉ Calculate (x)|.

[0132] As shown in the graph in Figure 8(a), the changes in each term at the current point in time are as follows: Δw １ f １ (x)=-1.2 Δw ２ f ２ (x) = +4 Δw ３ f ３ (x) = -6 Therefore, the change in each term (absolute value) is as follows: |Δw １ f １ (x) |=1.2 |Δw ２ f ２ (x) | = 4 | Δw ３ f ３ (x) | = 6 The normalization unit 17 is the largest change in history at the present time |Δw ｉ f ｉ (x) |ｍａｘ lol ｉ The effect of normalization is included in the coefficient by dividing by |Δw|. For example, the maximum change so far is |Δw|. １ f １ (x) | ｍａｘ = 2 | Δw ２ f ２ (x) | ｍａｘ Let's assume it was 1. The amount of change in this case is |Δw|. １ f １ (x) |=1.2 |Δw ２ f ２ (x)|=4, and |Δw ２ f ２ Regarding (x), the maximum change has been updated from 1 to 4, so as follows, w ２ Divide by the maximum value, w ２ Normalize and update it. ２ ←w ２ / |Δw ２ f ２ (x) | ｍａｘ lol ２ ←1 / 4 = 0.25 Note that the update of the coefficient for normalization in step S209 is performed only when the amount of change calculated in step S208 is the largest ever, and does not need to be performed if the amount of change calculated in step S208 is less than or equal to the largest ever.

[0133] <Calculation Example 3: Satisfaction Determination Before Coefficient Update> The objective function part 15 is the current best solution x with normalized coefficients. ｂｅｓｔ Change in each term |Δw ｉ f ｉ Calculate (x)|.

[0134] As shown in the graph in Figure 8(b), the changes in each term at this point in time are as follows: Δw １ f １ (x)=-1.2 Δw ２ f ２ (x) = +1 Δw ３ f ３ (x) = -6 Therefore, the change in each term (absolute value) is as follows: |Δw １ f １ (x) |=1.2 |Δw ２ f ２ (x) | = 1 | Δw３ f ３ (x)|=6 The satisfaction criterion is the same as in calculation formula 1, "change in high-priority terms > sum of changes in low-priority terms". Therefore, the satisfaction criterion is defined as |ΣiΔwifi(x)|>Σj|Δwjfj(x)| (i,j)=([1,2],[3]). Here, the change in high-priority terms is the sum of terms of equal priority (|Δw １ f １ (x) + Δw ２ f ２ (x)|)

[0135] Using the values ​​shown in the graph in Figure 8(b), the change in the high-priority term (|Δw) １ f １ (x) + Δw ２ f ２ (x)|) is 0.2 (=|-1.2+1|), and the sum of the changes in the lower priority terms |Δw| ３ f ３ (x)| is 6, and (|Δw １ f １ (x) + Δw ２ f ２ (x) |) < | Δw ３ f ３ (x)| (i.e., 0.2 < 6), so the criterion for satisfaction is not met.

[0136] Therefore, the automatic coefficient adjustment unit 16 determines that step S211 is NO, applies the coefficient update formula in step S212, and performs the coefficient update (coefficient adjustment) described below.

[0137] Furthermore, examining the change in the objective function at this time, we find that Δf ａｌｌ (x) = Δw １ f １ (x) + Δw ２ f ２ (x) + Δw ３ f ３ (x) = 0.2 - 6 < 0, and the higher priority is (w １ f １ (x) + w ２ f ２ Despite (x) worsening (increasing), the objective function f ａｌｌThis indicates an improvement (i.e., a decrease). In other words, the high-priority terms are not having an appropriate impact on the objective function.

[0138] <Calculation Example 3: Coefficient Update> In step S212, the automatic coefficient adjustment unit 16 automatically adjusts the coefficients so that "the amount of change in the high-priority term > the sum of the amounts of change in the low-priority term".

[0139] The change in the high-priority term is |Δw|. １ f １ (x) + Δw ２ f ２ (x)| and the sum of the changes in the low-priority terms is |Δw| ３ f ３ (x)|

[0140] And, as mentioned above, |Δw １ f １ (x) + Δw ２ f ２ (x) | < | Δw ３ f ３ Since (x) | (i.e., 0.2 < 6), the satisfaction criterion is not satisfied.

[0141] Furthermore, |Δw １ f １ (x) + Δw ２ f ２ (x) | ≠ 0, and | Δw ３ f ３ Since (x) ≠ 0, the coefficient automatic adjustment unit 16 calculates the coefficient w using the following formula. ３ Update. lol ３ ←w ３ ×|w １ f １ (x) + Δw ２ f ２ (x) | / | Δw ３ f ３ (x) | × c = 1 × (0.2 / 6) × 0.9 = 0.03 Therefore, the coefficient is w １ = 1.0, w ２ = 0.25, w ３ It will be updated to =0.03.

[0142] <Calculation Example 3: Priority Satisfaction Determination After Coefficient Update> The objective function part 15 is the best solution x at the present time with the adjusted coefficients. ｂｅｓｔChange in each term |Δw ｉ f ｉ Calculate (x)|.

[0143] As shown in Figure 8(c), the adjusted coefficient w １ = 1.0, w ２ = 0.25, w ３ The changes in each term when using = 0.03 are as follows: Δw １ f １ (x)=-1.2 Δw ２ f ２ (x) = +1 Δw ３ f ３ (x) = -0.18 Therefore, the change in each term (absolute value) is as follows: |Δw １ f １ (x) |=1.2 |Δw ２ f ２ (x) | = 1 | Δw ３ f ３ (x) | = 0.18 This is the satisfaction criterion (|Δw １ f １ (x) + Δw ２ f ２ (x) | > | Δw ３ f ３ (x)|) satisfies the condition. That is, (|-1.2+1|) > 0.18.

[0144] When we examine the change in the objective function at this time, we find that Δf ａｌｌ (x) = Δw １ f １ (x) + Δw ２ f ２ (x) + Δw ３ f ３ (x) = -1.2 + 1 - 0.18 < 0, and the higher priority is (w １ f １ (x) + w ２ f ２ Following the improvement (i.e., reduction) of the (x) term, the objective function Δf ａｌｌ (x) has also improved (i.e., decreased). This means that the high-priority terms are having an appropriate influence on the objective function. Therefore, we can conclude that we were able to adjust the weight coefficients of the objective function so that we can judge the improvement of the solution according to its priority.

[0145] <Application to Work Planning for Logistics Warehouses> The above section explains how to apply the method described above to the development of work plans for logistics warehouses.

[0146] Figure 9 is a diagram illustrating an example of the configuration of solution x according to Embodiment 1.

[0147] First, we define the work plan for the logistics warehouse as solution x. Solution x has a list structure in the program as shown in Figure 9. This list structure of solution x expresses who should perform which tasks, in what order, and at what time of day.

[0148] In Figure 9, the numbers such as "5:" and "6:" indicate worker numbers. Also, "A １３ "B" ５ "C １ The uppercase letters such as " indicate the type of work, and the alphabetical subscript numbers indicate the part number. Also, for the same part number p, A ｐ →CC ｐ , B ｐ →CC ｐ Assume there is an order constraint on A, and no other order constraints. For example, A １３ and A １４ The order of the elements can be rearranged.

[0149] And the objective function f is as follows: ａｌｌ We find the solution x that minimizes (x). This solution x that minimizes the objective function corresponds to the optimized work plan.

[0150] f ａｌｌ (x) = w １ f １ (x) + w ２ f ２ (x) + w ３ f ３ (x) + w ４ f ４ (x) + w ５ f ５ (x) f １ (x) is the term for the overdue time. Overdue time f １ (x) represents the total time spent working beyond the deadline.

[0151] f ２(x) is the term for the number of order violations. For the same part number, A ｐ →CC ｐ , B ｐ →CC ｐ There is an order constraint, and the number of order violations is f. ２ (x) will contain the number obtained by reversing this order constraint.

[0152] f ３ (x) is the term for working time. Working time f ３ (x) will contain the sum of the time spent on each task.

[0153] f ４ (x) is the term for the changeover time of the operation. Changeover time f ４ (x) represents the sum of each switching time.

[0154] f ５ (x) is the waiting time term. Waiting time f ５ (x) represents the total time between the possible start time and the actual start time. By minimizing this waiting time, a plan can be created to process tasks that can be done early as soon as possible.

[0155] The values ​​of each term in the objective function can be obtained by simulating the solution x using (1) the worker shift schedule and (2) information on each task (start time, deadline, work time, and transition time).

[0156] In this embodiment, the priority is f １ (x) = f ２ (x) > f ３ (x) = f ４ (x) > f ５ (x), that is, overdue time = number of order violations > work time = changeover time > waiting time. With this priority, the constraint term overdue time f should be set to zero in order to obtain a feasible plan. １ (x), number of order violations f ２ (x) is given the highest priority, and the waiting time f is a term that does not interfere with the plan. ５ The priority of (x) is set to the lowest.

[0157] Figure 10 shows an example of the work plan generation result according to Embodiment 1. Figure 11 is a graph showing the changes in each term when the automatic coefficient adjustment is applied and the planning loop is repeated according to Embodiment 1. Figure 12 is a graph showing the changes in each term when a skilled technician manually adjusts the coefficients and the planning loop is repeated.

[0158] The planning device 10 uses the objective function f ａｌｌ (x) = w １ f １ (x) + w ２ f ２ (x) + w ３ f ３ (x) + w ４ f ４ (x) + w ５ f ５ The solution x that minimizes (x) is calculated, and a work plan like the one shown in Figure 10 is generated based on the calculated solution x. The work plan shown in Figure 10 indicates which worker(s) will perform which tasks, in what order, and at what time of day.

[0159] In the graphs shown in Figures 11 and 12, the vertical axis represents the evaluation value or penalty value of the term (i.e., w). ｉ f ｉ The graph shows the value of (x), and the horizontal axis shows the number of plan updates (i.e., the number of iterations in the planning loop). However, the value on the vertical axis is divided by the maximum value to be in the range of [0, 1]. Each line in the graphs shown in Figures 11 and 12 corresponds to each term of the objective function.

[0160] The graph of automatic coefficient adjustment shown in Figure 11, like the graph of manual coefficient adjustment shown in Figure 12, initially shows the deadline overdue time f with the highest priority. １ (x) and the number f that violates order ２ The (x) term decreases to zero. Subsequently, the graph of the automatic coefficient adjustment shown in Figure 11 shows the second highest priority work time f. ３ (x), switching time f ４ The term (x) is decreasing. And the waiting time f has the lowest priority. ５ The term (x) shows an increasing trend as a result of prioritizing the decrease of the other terms.

[0161] Furthermore, as shown in the graph within the dotted line frame 301 in Figure 11, even terms with different units, such as the deadline overrun time f1(x) and the number of order violations f2(x), can be minimized while maintaining a balance of changes in each term by applying normalization.

[0162] Thus, when the automatic coefficient adjustment according to this embodiment is applied, it is possible to determine coefficients that can appropriately influence the objective function in order of priority, similar to when a skilled technician manually adjusts the coefficients. Therefore, according to this embodiment, a planning device 10 can be provided that allows even inexperienced technicians to easily create work plans.

[0163] <Input Screen for Priority and Normalization Flags> Next, we will explain the input screen for users to enter priority and normalization flags.

[0164] Figure 13 is a diagram showing a first example of an input screen according to Embodiment 1.

[0165] The input unit 14 displays an input screen 100A, as shown in Figure 13, on the display device 1005 (see Figure 18). The input screen 100A displays the priority input area 101.

[0166] As shown in Figure 13, when the user enters [[3,5],[1,2],[4]] into the priority input area 101, f ３ (x) term = f ５ (x) term > f １ (x) term = f ２ (x) term > f ４ The priority of the (x) term is set. In other words, the contents of the priority input area 101 are such that the sequence of numbers in the outer parentheses indicates the priority rank, and the sequence of numbers in the inner parentheses indicates that the priorities are equal.

[0167] Figure 14 shows a second example of the input screen according to Embodiment 1.

[0168] The input unit 14 displays a priority input screen 100B, as shown in Figure 14, on the display device 1005 (see Figure 18). The input screen 100B displays a priority input area 101 and a normalization flag input area 102.

[0169] The priority input area 101 is the same as in Figure 13.

[0170] As shown in Figure 14, when the user enters [[1,1],[0,0],[0]] into the normalization flag input area 102, the f corresponding to "1" in the normalization flag input area 102 is selected from among the [[3,5],[1,2],[4]] entered into the priority input area 101. ３ (x) term and f ５ The (x) term is set to be normalized. In other words, the contents of the normalization flag input area 102 indicate that the term in the priority input area 101 corresponding to "1" is the one to be normalized.

[0171] Figure 15 shows a third example of the input screen according to Embodiment 1.

[0172] The input unit 14 displays a priority input screen 100C, as shown in Figure 15, on the display device 1005 (see Figure 18). The input screen 100C displays a priority input area 103 corresponding to each item.

[0173] As shown in Figure 14, the user enters f in the priority input area 103. １ , f ２ , f ３ , f ４ , f ５ By inputting 2, 2, 1, 3, 1 corresponding to each of them, f ３ (x) term = f ５ (x) term > f １ (x) term = f ２ (x) term > f ４ The priority of item (x) is set. In other words, the contents of the priority input area 103 show the priority value of each item.

[0174] Figure 16 shows a fourth example of the input screen according to Embodiment 1.

[0175] The input unit displays a priority input screen 100D, as shown in Figure 16, on the display device 1005 (see Figure 18). The input screen 100D displays a priority input area 103 and a normalization flag input area 104 corresponding to each item.

[0176] The priority input area 103 is the same as in Figure 15.

[0177] As shown in Figure 16, the user enters f in the normalization flag input area 104. １ , f ２ , f ３ , f ４ , f ５ By inputting 0, 0, 1, 1, 0 corresponding to each of these, the f corresponding to "1" in the normalization flag input area 104 ３ (x) term and f ５ The (x) term is set to be normalized. In other words, the content of the normalization flag input area 104 indicates that the term corresponding to "1" is the one to be normalized.

[0178] Figure 17 shows a fifth example of the input screen according to Embodiment 1.

[0179] The input unit 14 displays a priority input screen 100E, as shown in Figure 17, on the display device 1005 (see Figure 18). The input screen 100E displays a priority input area 103 and a unit input area 105 corresponding to each item.

[0180] The priority input area 103 is the same as in Figure 15.

[0181] As shown in Figure 17, the user enters f in the unit input area 105. １ , f ２ , f ３ , f ４ , f ５ The units are entered corresponding to each. The normalization unit 17 sets terms with different units to be normalized. For example, in the input screen 100E shown in Figure 17, f ５ The corresponding unit "cnt" (number) is other f １ , f ２ , f ３ , f ４ Since it is different from the corresponding unit "sec" (second), the normalization unit 17 f ５ This will be the target of normalization.

[0182] <Hardware Configuration> Figure 18 is a block diagram showing the hardware configuration of a computer that implements the functions of the planning device 10 according to this disclosure using a computer program.

[0183] The computer 1000 includes a processor 1001, memory 1002, storage 1003, input device 1004, display device 1005, and communication device 1006.

[0184] The processor 1001 is a device that executes a computer program stored in the memory 1002 and realizes the functions of the planning device 10 described above. The processor 1001 may also be read as a Central Processing Unit (CPU), controller, control unit, or control device. Furthermore, the processor 1001 may include a Graphics Processing Unit (GPU) and / or a Neural Processing Unit (NPU).

[0185] The memory 1002 is composed of a volatile storage medium and / or a non-volatile storage medium, and is a device for storing computer programs and data handled by the computer 1000.

[0186] The storage device 1003 is composed of a non-volatile storage medium and is a device that stores computer programs and data handled by the computer 1000. Examples of the storage device 1003 include a Hard Disk Drive (HDD), a Solid State Drive (SSD), or flash memory.

[0187] The input device 1004 is a device that receives data from the user to be input to the processor 1001. Examples of input devices 1004 include a keyboard, mouse, touchpad, and microphone. For example, the user inputs data through the input device 1004 to the input screens shown in Figures 13 to 17.

[0188] The display device 1005 is a device that displays data generated by the processor 1001. Examples of the display device 1005 include liquid crystal displays and organic EL displays. For example, the planning device 10 displays the input screens shown in Figures 13 to 17 on the display device 1005.

[0189] The communication device 1006 is a device that transmits and receives data to and from other devices, such as a server device, via a communication network. Examples of communication networks include wired LANs, wireless LANs, the Internet, mobile communication networks, or Bluetooth®.

[0190] (Summary of Embodiment 1) The following technology is disclosed based on the description of Embodiment 1 above.

[0191] <Technology 1> An improvement device according to one embodiment (for example, an improvement determination unit 13) has multiple items (for example, w ｉ f ｉ (x)) includes an objective function (e.g., f ａｌｌ An input unit (14) accepts the specification of the priority of each term for (x), and the objective function has a predetermined solution (for example, the best solution x) ｂｅｓｔ The result of applying ) and the comparative solution (x) obtained by rearranging the solution in the objective function ｔｍｐ The change in each of the above terms (e.g., |Δw) between the result obtained by applying ) and the result obtained by applying ) ｉ f ｉ The system includes an adjustment unit (e.g., an automatic coefficient adjustment unit 16) that calculates (x) and adjusts the objective function such that if the change in the first priority term is less than the sum of the changes in the terms with a lower priority than the first priority, the change in the first priority term is greater than the sum of the changes in the terms with a lower priority than the first priority.

[0192] This allows the adjustment unit to automatically adjust the objective function.

[0193] <Technology 2> In the improvement device described in Technology 1, if the terms with a priority lower than the first priority include the terms with the second priority and terms with a priority lower than the second priority, the adjustment unit adjusts the objective function so that the amount of change in the first priority term is greater than the sum of the amounts of change in the terms with a priority lower than the first priority, and then adjusts the objective function so that the amount of change in the second priority term is greater than the sum of the amounts of change in the terms with a priority lower than the second priority.

[0194] This allows the adjustment unit to automatically adjust the objective function.

[0195] <Technology 3> In the improvement device described in Technology 1 or 2, if there are multiple terms with the same priority level, the adjustment unit treats the terms with the same priority level as a single term and adjusts the objective function.

[0196] This allows the adjustment unit to automatically adjust the objective function.

[0197] <Technology 4> The improvement device described in any one of Techniques 1 to 3 further comprises an improvement determination unit (13) that updates the solution to the comparison solution if the result of applying the comparison solution to the objective function is an improvement over the result of applying the solution to the objective function, and the adjustment unit further calculates the amount of change and adjusts the objective function using the updated solution.

[0198] This allows the improvement device to automatically adjust the objective function while searching for a more optimal solution.

[0199] <Technology 5> In the improvement device described in Technology 4, the processing by the improvement determination unit and the processing by the adjustment unit are repeated multiple times, and the adjustment unit further normalizes each term of the objective function using the maximum amount of change calculated for each term in the repetition, recalculates the amount of change using the normalized objective function, and adjusts the objective function based on the recalculated amount of change.

[0200] This allows the improvement device to normalize the terms in the objective function.

[0201] <Technical 6> In the improvement device described in Technical 5, the adjustment unit performs the normalization when the terms included in the objective function include terms with different units.

[0202] This allows the adjustment unit to normalize terms with different units included in the objective function.

[0203] <Technology 7> In the improvement device described in any one of Techniques 1 to 6, the objective function is a weighted sum of functions using the solutions corresponding to each term, and the adjustment unit adjusts the objective function by adjusting the weights of each term.

[0204] This allows the adjustment unit to automatically adjust the objective function.

[0205] <Technology 8> In the improvement device described in any one of Technology 1 to 7, the input unit displays an input screen that accepts the specification of the priority of each of the above items.

[0206] This allows users to specify the priority of each item through the input screen.

[0207] <Technology 9> In the improvement device described in any one of Techniques 1 to 7, the input unit displays an input screen that accepts the specification of the priority of each item and the specification of the item to be normalized.

[0208] This allows users to specify the priority of each item and the items to be normalized through the input screen.

[0209] <Technical 10> An improvement method according to one embodiment accepts the designation of the priority of each term for an objective function that includes multiple terms, calculates the change in each term between the result of applying a predetermined solution to the objective function and the result of applying a comparative solution obtained by rearranging the solution to the objective function, and if the change in the term with the first priority is less than the sum of the change in the terms with a lower priority than the first priority, the objective function is adjusted so that the change in the term with the first priority is greater than the sum of the change in the terms with a lower priority than the first priority.

[0210] This allows the objective function to be automatically adjusted.

[0211] <Technical 11> A planning device according to one embodiment includes: an input unit that accepts the specification of the priority of each term for an objective function that includes a plurality of terms; an adjustment unit that calculates the amount of change for each term between the result of applying a predetermined solution to the objective function and the result of applying a comparison solution obtained by rearranging the solution to the objective function, and adjusts the objective function so that the amount of change of the term with first priority is greater than the sum of the amounts of change of the terms with priority lower than the first priority if the amount of change of the term with first priority is less than the sum of the amounts of change of the terms with priority lower than the first priority; an improvement determination unit that updates the solution to the comparison solution if the result of applying the comparison solution to the objective function is an improvement over the result of applying the solution to the objective function; and a planning generation unit that generates a plan based on the solution obtained after repeating the processing by the improvement determination unit and the processing by the adjustment unit multiple times.

[0212] This allows the planning system to create an improved plan based on the explored solutions, with the objective function automatically adjusted.

[0213] While embodiments have been described above with reference to the attached drawings, this disclosure is not limited to such examples. It will be clear to those skilled in the art that various modifications, alterations, substitutions, additions, deletions, and equivalents can be conceived within the scope of the claims, and these will also be understood to fall within the technical scope of this disclosure. Furthermore, the components of the embodiments described above may be combined in any way without departing from the spirit of the invention.

[0214] The technology disclosed herein is useful for developing improved plans using objective functions.

[0215] 10 Planning device 11 Initial planning unit 12 Plan rearrangement unit 13 Improvement judgment unit 14 Input unit 15 Objective function unit 16 Coefficient automatic adjustment unit 17 Normalization unit 18 Plan generation unit 100A, 100B, 100C, 100D, 100E Input screen 101, 103 Priority input area 102, 104 Normalization flag input area 105 Unit input area 1000 Computer 1001 Processor 1002 Memory 1003 Storage 1004 Input device 1005 Display device 1006 Communication device

Claims

An input section that accepts the specification of the priority of each term in an objective function containing multiple terms, The amount of change for each term is calculated between the result of applying a predetermined solution to the objective function and the result of applying a comparative solution obtained by rearranging the solution to the objective function. If the change in the first priority term is less than the sum of the changes in the terms with a lower priority than the first priority, the adjustment unit adjusts the objective function so that the change in the first priority term is greater than the sum of the changes in the terms with a lower priority than the first priority. An improvement device equipped with the following features. 　 The adjustment unit is, If the terms with a lower priority than the first priority include terms with a second priority and terms with a lower priority than the second priority, After adjusting the objective function so that the change in the first priority term is greater than the sum of the changes in the terms with lower priority than the first priority, the objective function is then adjusted so that the change in the second priority term is greater than the sum of the changes in the terms with lower priority than the second priority. The improvement device according to claim 1. 　 The adjustment unit is, If there are multiple terms with the same priority level, the terms with the same priority level are treated as a single term, and the objective function is adjusted accordingly. The improvement device according to claim 1. 　 The system further includes an improvement determination unit that updates the solution to the comparison solution if the result of applying the comparison solution to the objective function is better than the result of applying the solution to the objective function, The adjustment unit further calculates the amount of change and adjusts the objective function using the updated solution. The improvement device according to any one of claims 1 to 3. 　 The process by the improvement determination unit and the process by the adjustment unit are repeated multiple times. The adjustment unit further normalizes each term of the objective function using the maximum change calculated for each term in the iteration, recalculates the change using the normalized objective function, and adjusts the objective function based on the recalculated change. The improvement device according to claim 4. 　 The adjustment unit performs the normalization when the terms included in the objective function include terms with different units. The improvement device according to claim 5. 　 The objective function is a weighted sum of functions using the solutions corresponding to each term. The adjustment unit adjusts the objective function by adjusting the weights of each term. The improvement device according to claim 1. 　 The input unit displays an input screen that accepts the specification of the priority of each of the above items. The improvement device according to claim 1. 　 The input unit displays an input screen that accepts the specification of the priority of each item and the specification of the items to be normalized. The improvement device according to claim 5. 　 For objective functions containing multiple terms, we accept the specification of the priority of each term. The amount of change for each term is calculated between the result of applying a predetermined solution to the objective function and the result of applying a comparative solution obtained by rearranging the solution to the objective function. If the change in the first priority term is less than the sum of the changes in the terms with a lower priority than the first priority, the objective function is adjusted so that the change in the first priority term is greater than the sum of the changes in the terms with a lower priority than the first priority. How to improve. 　 An input section that accepts the specification of the priority of each term in an objective function containing multiple terms, The amount of change for each term is calculated between the result of applying a predetermined solution to the objective function and the result of applying a comparative solution obtained by rearranging the solution to the objective function. If the change in the first priority term is less than the sum of the changes in the terms with a lower priority than the first priority, the adjustment unit adjusts the objective function so that the change in the first priority term is greater than the sum of the changes in the terms with a lower priority than the first priority. If the result of applying the comparison solution to the objective function is improved compared to the result of applying the solution to the objective function, the improvement determination unit updates the solution to the comparison solution. The system includes a planning generation unit that generates a plan based on the solution after the processing by the improvement determination unit and the processing by the adjustment unit have been repeated multiple times. Planning device.

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